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Is AI ready to leave human in the loop behind?

Merlin Peter
April 20, 2021

For all the incredible things AI has achieved today, humans have played an integral role in designing flawless algorithms behind the scenes. From Alexa switching on our lights using voice commands to robots delivering food at our doorsteps, and YouTube or Spotify recommending our favourite songs, artificial intelligence has automated our daily lives in more ways than we are willing to admit. Even for important business decisions and other critical medical diagnoses, and drug discoveries, humans are increasingly relying on artificial intelligence to lead the way.   

While there’s nothing wrong with this tech-led approach, many are worried about AI infiltrating our lives beyond control and ultimately replacing the human in the loop throughout the economy. But this is far from the truth. 

Addressing the elephant in the room: Is AI ready to leave human in the loop behind? 

The short answer is no. We are certain that AI is not dismissing its most trusted workforce anytime soon. In fact, AI systems are complementing and augmenting human capabilities, not replacing them. With AI, the nature of our work may change in the years to come. However, the fundamental principle is the elimination of mundane tasks and increased efficiency for tasks where human inputs are indispensable. 

Unsupervised machine learning is feasible for low-stake tasks like Netflix recommendations or Siri’s voice control functions. Netflix or Apple is not going to lose lives or money if their algorithms don’t get a few functions right. But for more critical functions like self-driving cars or disease diagnoses, there’s no scope for errors. Accuracy and quality become more important in such cases. This is where human-in-the-loop comes in. 

Machine learning systems that rely on supervised learning integrate both human and machine intelligence to provide more accurate results. The human in the loop is continuously creating labeled datasets, training, testing, and validating the ML algorithms. The models become smart and perform real-life functions over a period of time. But the influx of human-annotated training data is constantly required to train the algorithms about our changing environments. 

| You can read more about human in the loop for machine learning in our previous article. 

Here are more reasons why the human in the loop cannot be dismissed yet. 

It’s true. Well-trained ML models can help enhance our decision-making and cognitive aptitudes. They increase our physical capabilities, ease the load of mundane tasks, and free our excellence for higher-stake tasks. But it’s practically impossible to teach machines to do all of this without human in the loop. 

For AI systems to function well, it’s crucial that human experts train machines methodically. ML algorithms need inputs in the language it understands to provide logical outputs that help carry out real-life functions seamlessly. Here, accurate data labeling plays a pivotal role in helping machines comprehend human use cases and scenarios. 

Basic Human-in-the-loop Data Labeling Workflow

For instance, human annotators create bounding box annotations for computer vision models in self-driving cars or robots. The annotations help the models recognise shapes and identify objects more precisely. Semantic segmentation techniques are used to classify objects pertaining to a similar class for visual perception models. Similarly, landmark or keypoint annotations are used to create facial recognition training datasets. 

| Learn more about image, video, and sensor fusion annotations for computer vision models.

In language learning or speech recognition models, sentiment analysis or NLP techniques are used as inputs. These training datasets help machines understand what humans are saying in different scenarios. 

In more complex machine learning models that use opaque computing processes, humans are required to solve the black box problem. To put it more simply, we require human inputs to decode how a program arrived at a conclusion when practical, legal, or theoretical consequences are in question. For example, consider medical diagnoses produced via AI systems. A practitioner must understand the underlying principles the model used to identify the diagnoses from the inputs provided. He/she also needs to validate if the medical recommendations are practically feasible or safe. 

As AI is being integrated into more and more fields, the problems surrounding ethics, biases, privacy, compliance, etc need to be addressed. Human in the loop will be required to tackle these problems to help AI systems coexist with humans in a manner that is safe and responsible. 

| Here’s how Playment is complying with GDPR to ensure data security for its customers.

Collaborative intelligence will fuel AI systems in the years to come.

The progress we’ve achieved today with AI can be largely attributed to the collaborative intelligence of humans and machines. Machines offer speed, scale, and operational flexibility to execute tasks. Humans can use their creativity, judgment, leadership, and other skills to use AI responsibly and make business processes more efficient. The key to success lies in optimising these combined forces and creating an intelligent human-machine interface. 

Here’s how Playment is leveraging collaborative intelligence to optimise data labeling for computer vision models.  

At Playment, we’ve created an intelligent mix of sophisticated annotation tools and human operational expertise to optimise data labeling pipelines. 

We understand the importance of high-quality labeled datasets and their role in building functional computer vision models for real-life scenarios. And that’s why we’ve mastered the art of mixing the strengths of both humans and machines in the most optimal way. We believe that collaborative intelligence was, is, and will be the backbone of successful AI systems in the years to come.

What do you think? Write to us at, we’d love to know your thoughts.